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International Conference on Electrical engineering, computer science and Informatics (EECSI)

作者： Shahed Mohammadi Niloufar Hemati Ali Hashemi Haniye Zandiye Computer Science & Systems Engineering Dep. Ayandegan University Mazandaran Iran Islamic Azad University Central Tehran Branch Tehran Iran Qom University of Technology Qom Iran

This paper presents a study on speech emotion recognition using deep learning techniques. The focus is on utilizing the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) and the Toronto Emotional Speech Set (TESS) datasets. The proposed approach involves preprocessing the audio signals by converting them into a sequence of numbers and normalizing them using max normalization. Furthermore, the signals are augmented using three methods: noise with a 0.015 noise rate, pitch adjustment, and a combination of noise with a 0.03 noise rate and pitch adjustment. The extraction of audio features includes a combination of two features, namely Mel Frequency Cepstrum Coefficients (MFCC) and Root Mean Square (RMS) energy, which provide distinct information from each other. These features are then used to train a CNN for emotion classification. Experimental results demonstrate the effectiveness of the proposed approach in accurately recognizing emotions from speech signals. The proposed approach achieves an accuracy of 93.6% on the RAVDESS dataset and 99.9% on the TESS dataset. These results highlight the potential of the proposed approach in practical applications such as speech therapy and human-computer interaction.

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A Model Based Framework for Testing Safety and Security in Operational Technology Environments

arXiv

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arXiv 2023年

作者： Bhole, Mukund Kastner, Wolfgang Sauter, Thilo Institute of Computer Engineering TU Wien Vienna Austria Institute of Computer Technology TU Wien Dep. of Integrated Sensor Systems Danube University Krems Austria

Today’s industrial control systems consist of tightly coupled components allowing adversaries to exploit security attack surfaces from the information technology side, and, thus, also get access to automation devices residing at the operational technology level to compromise their safety functions. To identify these concerns, we propose a model-based testing approach which we consider a promising way to analyze the safety and security behavior of a system under test providing means to protect its components and to increase the quality and efficiency of the overall system. The structure of the underlying framework is divided into four parts, according to the critical factors in testing of operational technology environments. As a first step, this paper describes the ingredients of the envisioned framework. A system model allows to overview possible attack surfaces, while the foundations of testing and the recommendation of mitigation strategies will be based on process-specific safety and security standard procedures with the combination of existing vulnerability databases. © 2023, CC BY.

关键词： Model checking

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Networked multi-sensor rollator with AI-assisted fall detection

Networked multi-sensor rollator with AI-assisted fall detect...

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Annual IEEE Symposium on computer-Based Medical systems

作者： Markus Schwarz Vincent Gauda Markus Kaiser Henrik K. J. Kuttruff Jonas Rentzsch Maximilian Thür Dep. of Health Care Hochschule Niederrhein Univ. of Applied Sciences Krefeld Germany Dep. of Electrical Engineering and Computer Science Hochschule Niederrhein Univ. of Applied Sciences Krefeld Germany

This contribution describes a networked and energy-autonomous rollator, primarily designed to increase the users safety. To achieve this, a multi-sensor approach is used to provide sufficient data for classification of activity patterns, detection of long term changes in agility as well as fall detection, supported by AI technology. For data economy, networking of the rollator in outdoor areas is limited to the transmission of emergency situations via mobile radio to inform caregivers. With the user's consent, analysis of medium-and long-term changes in movement and activity behaviour can also be transmitted as an option.

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EEG-Based Stress Assessment During Robot Assisted Surgery. Comparison of Statistical Methods with Machine Learning

EEG-Based Stress Assessment During Robot Assisted Surgery. C...

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Metrology for Extended Reality, Artificial Intelligence and Neural engineering (MetroXRAINE), IEEE International Conference on

作者： Pasquale Arpaia Irene Del Chicca Matteo De Luca Ludovica Gargiulo Giovanna Mastrati Nicola Moccaldi Elisa Morganti Marco Nalin Mauro Picciafuoco Francesca Pierro Filippo Rigoni Riccardo Rigoni Dep. of Electrical Engineering and Information Technology (DIETI) University of Naples Federico II Naples Italy ab medica S.p.A. Cerro Maggiore MI Italy Dep. of Electronics Information and Bioengineering (DEIB) Polytechnic University of Milan Milan Italy Dep. of Computer Science (D-INFK) ETH Zurich Zurich Switzerland

ISBN: (数字)9798350378009

ISBN: (纸本)9798350378016

This study proposes a adaptive pipeline (feature extraction, feature selection, and classification) for the processing of electroencephalographic (EEG) signals to assess Mental Workload (MWL) during training sessions with a surgical robot. Unlike previous studies in this field, EEG signals were acquired by a dry eight-electrode device to maximize usability and comfort for the users. Fourteen medical students were EEG monitored while performing four different tasks on a simulation platform incorporated into the Da Vinci System. The proposed pipeline was compared with typical machine-learning approaches based on handcraft feature extraction approaches. The results confirmed the effectiveness of the proposed solution as a further contribution to EEG-based monitoring of robotic surgery for adaptive training.

关键词： Training Medical robotics Pipelines Surgery Machine learning Feature extraction Electroencephalography Biomedical monitoring Usability Monitoring

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Hybrid Algorithm for Real-Time Data Forecasting with Density-Based Clustering and Penalty Splines

Hybrid Algorithm for Real-Time Data Forecasting with Density...

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International Conference on Industrial engineering, Applications and Manufacturing (ICIEAM)

作者： Elena Kochegurova Vladislav Denisov Maria Galkina Information Systems and Technologies Dep. Tomsk Polytechnic University Tomsk Russian Federation School of Nuclear Science and Engineering Tomsk Polytechnic University Tomsk Russian Federation

ISBN: (数字)9798350395013

ISBN: (纸本)9798350395020

We propose a hybrid forecasting model that includes time series segmentation, which is based on the DBScan clustering algorithm, and a penalty spline. Each step of the hybrid model required modification for real-time use. The DBScan density-based algorithm was supplemented with technology for working with noise and transition between clusters. A recurrent penalty spline model has low computational complexity and, accordingly, high forecasting speed inside the hybrid model. The forecast efficiency was assessed by a number of indicators that reflect predictive efficiency and extrapolation accuracy of using synthetic, model and real TS. Accuracy indicators (MSPE, MAPE) are below 4%. In comparison with other widely-spread forecasting methods, the proposed hybrid algorithm ranks among the known algorithms.

关键词： Extrapolation Accuracy Computational modeling Noise Clustering algorithms Predictive models Prediction algorithms

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Parallel and hierarchically-distributed Shoreline Alert Model (SAM)

Parallel and hierarchically-distributed Shoreline Alert Mode...

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Euromicro Conference on Parallel, Distributed and Network-Based Processing

作者： Ciro Giuseppe de Vita Gennaro Mellone Aniello Florio Catherine Alessandra Torres Charles Diana Di Luccio Marco Lapegna Guido Benassai Giorgio Budillon Raffaele Montella Dep. of Science and Technologies University of Naples “Parthenope” Naples Italy Dep. of Computer Science and Engineering University of Madrid Carlos III Madrid Spain Dept. of Mathematics and Applications University of Naples “Federico II” Naples Italy

In this paper, the Shoreline Alert Model (SAM) is presented as a component of a computation platform based on workflows dedicated to extreme weather/marine event simulation. The model aims to mitigate the effects of global change by providing decision-makers, scientists, and engineers with a novel, next-generation tool set for facing extreme weather events and implementing related management or emergency responses. SAM uses a parallelization schema, allowing users to run it on heterogeneous parallel architectures. As a result, SAM produces approximately 24 times faster results than the baseline when using shared memory with distributed memory and dealing with about 20,000 transects along the Campania coastline. The system is based on the algorithms of the open-source numerical models WRF (Weather Research and Forecasting) and WW3 (Wave-watch III) implemented with refraction and shoaling routines together with run-up equations to form the modeling chain used for coastal flooding assessment.

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A Proposed Approach for Risk Planning and Management

A Proposed Approach for Risk Planning and Management

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Information Technology (ACIT)

作者： Mohammed N. Al Refai Yazan Al-Smadi Adai Al-Momani Zeyad M. Jamhawi Ahmed Ali Otoom Issa Atoum Ahid Yaseen Jafar Ababneh Mohammad Kanan Software Engineering Dep. Zarqa University Zarqa Jordan Department of Computer Science Zarqa University Zarqa Jordan Department of Computer Science Applied Science Private University Amman Jordan Department of Cyber Security Applied Science Private University Amman Jordan Software Engineering Department Faculty of Information Technology Philadelphia University Jordan Department of Cyber Security Zarqa University Zarqa Jordan Dep. of Industry Engineering University of Business and Technelogy Jeddah Saudi Arabia

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Project Risk management is the process of identifying, evaluating, avoiding, or reducing risks. Where there is no software project without risks existence are natural in the context of project planning and management. This study focuses on proposing an enhanced risk management approach where testing phases were added to make the risk management process more effective by proposing solutions to many mistakes and challenges that occur through dealing with risks. The proposed approach focuses on dealing with risks that cannot be easily defined, more accurate risk evaluation, planning, and managing risk coordinated with a contingency plan.

关键词： Technological innovation Accuracy Contingency management Process control Software Planning Risk management Information technology Testing

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A Novel Gpu-Based Approach for Embedded Narmax/Frols System Identification

SSRN

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SSRN 2023年

作者： Soudré, Marlon Marques Ayala, Helon Vicente Hultmann Melo, Alba Cristina M.A. Llanos, Carlos H. University of Brasilia Dep. of Mechanical Engineering DF Brasilia70910-900 Brazil Pontifical Catholic University of Rio de Janeiro Dep. of Mechanical Engineering RJ Rio de Janeiro22231-180 Brazil University of Brasilia Dep. of Computer Science DF Brasilia70910-900 Brazil

Identifying system dynamics is an essential task for several research areas that deal with real systems intended for purposes such as control, monitoring, or fault detection. However, the nonlinear characteristics of most real systems have led to the development of techniques with high computational complexity, which can make their use unfeasible in systems with restrictions, such as embedded systems. In this case, few studies have been done on how best to map nonlinear system identification techniques to embedded system platforms which may have parallelism capabilities, such as those incorporating GPUs. This work proposes a novel software architecture for parallel model term selection and estimation for Nonlinear AutoRegressive Moving Average with eXogenous inputs (NARMAX) models, addressed to GPU-based embedded platforms. Namely, we devise a new parallel strategy to perform Forward Regression Orthogonal Least Squares (FROLS) in nonlinear black-box system identification. The architecture presented was tested in the new NVIDIA Jetson Developer Kit, which contains a 128-core Maxwell-GPU, and compared to serial versions of the algorithm run in standard Intel Core i7 CPU and ARM A57. Using a theoretical example, we defined a parallelization strategy and validated it with an electro-mechanical positioning system representing time-invariant nonlinear systems. As a result, the time spent in the proposed parallel GPU algorithm is more than 24.5x faster, and the energy-efficiency is 22x better if compared to its serial version. This represents a significant improvement in speedup and energetic demand, allowing NARMAX/FROLS models to be constructed in real-time using low-cost and low-power embedded platforms. © 2023, The Authors. All rights reserved.

关键词： Electric power utilization

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Analyzing Data Efficiency and Performance of Machine Learning Algorithms for Assessing Low Back Pain Physical Rehabilitation Exercises

Analyzing Data Efficiency and Performance of Machine Learnin...

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European Conference on Mobile Robots (ECMR)

作者： Aleksa Marusic Louis Annabi Sao Mai Nguyen Adriana Tapus Autonomous Systems and Robotics Lab Computer Science and System Engineering (U2IS) ENSTA Paris Institut Polytechnique de Paris Palaiseau France Dep. Informatique IMT Atlantique

Physical rehabilitation focuses on the improvement of body functions, usually after injury or surgery. Patients undergoing rehabilitation often need to perform exercises at home without the presence of a physiotherapist. computer-aided assessment of physical rehabilitation can improve patients' performance and help in completing prescribed rehabilitation exercises. In this work, we focus on human motion analysis in the context of physical rehabilitation for Low Back Pain (LBP). As 2D and 3D human pose estimation from RGB images had made impressive improvements, we aim to compare the assessment of physical rehabilitation exercises using movement data acquired from RGB videos and human pose estimation from those. In this work, we provide an analysis of two types of algorithms on a Low Back Pain rehabilitation datasets. One is based on a Gaussian Mixture Model (GMM), with performance metrics based on the log-Likelihood values from GMM. Furthermore, with the recent development of Deep Learning and Graph Neural Networks, algorithms based on Spatio-Temporal Graph Convolutional Networks (STGCN) are taken as a novel approach. We compared the algorithms in terms of data efficiency and performance, with evaluation performed on two LBP rehabilitation datasets: KIMORE and Keraal. Our study confirms that Kinect, OpenPose, and BlazePose data yield similar evaluation scores, and shows that STGCN outperforms GMM in most configurations.

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Approaches to Quantum Remote Memory Attestation

arXiv

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arXiv 2025年

作者： Laeuchli, Jesse Rasua, Rolando Trujillo The School of Computer Science and Engineering UNSW Sydney Sydney Australia The Dep. Enginy. Informàtica i Matemàtiques Rovira i Virgili University Tarragona Spain

In this article we uncover flaws and pitfalls of a quantum-based remote memory attestation procedure for Internet-of-Things devices. We also show limitations of quantum memory that suggests the attestation problem for quantum memory is fundamentally different to the attestation problem for classical memory, even when the devices can perform quantum computation. The identified problems are of interest for quantum-based security protocol designers in general, particularly those dealing with corrupt devices. Finally, we make use of the lessons learned to design a quantum-based attestation system for classical memory with improved communication efficiency and security. © 2025, CC BY.

关键词： Quantum computers

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